Dataset statistics
| Number of variables | 13 |
|---|---|
| Number of observations | 81 |
| Missing cells | 170 |
| Missing cells (%) | 16.1% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 8.4 KiB |
| Average record size in memory | 105.6 B |
Variable types
| NUM | 10 |
|---|---|
| CAT | 3 |
Urban area[12] Population is highly correlated with Metropolitan area[d] Population | High correlation |
Metropolitan area[d] Population is highly correlated with Urban area[12] Population | High correlation |
City proper[c] Definition is highly correlated with City[a] | High correlation |
City[a] is highly correlated with City proper[c] Definition | High correlation |
City proper[c] Definition has 6 (7.4%) missing values | Missing |
City proper[c] Population has 7 (8.6%) missing values | Missing |
City proper[c] Area (km2) has 7 (8.6%) missing values | Missing |
City proper[c] Density (/km2) has 7 (8.6%) missing values | Missing |
Metropolitan area[d] Population has 40 (49.4%) missing values | Missing |
Metropolitan area[d] Area (km2) has 50 (61.7%) missing values | Missing |
Metropolitan area[d] Density (/km2) has 50 (61.7%) missing values | Missing |
Urban area[12] Population has 1 (1.2%) missing values | Missing |
Urban area[12] Area (km2) has 1 (1.2%) missing values | Missing |
Urban area[12] Density (/km2) has 1 (1.2%) missing values | Missing |
City[a] has unique values | Unique |
Reproduction
| Analysis started | 2021-08-17 18:18:03.860784 |
|---|---|
| Analysis finished | 2021-08-17 18:18:20.944068 |
| Duration | 17.08 seconds |
| Software version | pandas-profiling v2.9.0 |
| Download configuration | config.yaml |
| Distinct | 81 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 648.0 B |
| London | 1 |
|---|---|
| Mumbai | 1 |
| Bangkok | 1 |
| Rio de Janeiro | 1 |
| Dongguan | 1 |
| Other values (76) |
| Value | Count | Frequency (%) | |
| London | 1 | 1.2% | |
| Mumbai | 1 | 1.2% | |
| Bangkok | 1 | 1.2% | |
| Rio de Janeiro | 1 | 1.2% | |
| Dongguan | 1 | 1.2% | |
| Hyderabad | 1 | 1.2% | |
| Lima | 1 | 1.2% | |
| Beijing | 1 | 1.2% | |
| Singapore | 1 | 1.2% | |
| Shenyang | 1 | 1.2% | |
| Other values (71) | 71 | 87.7% |
Frequencies of value counts
Unique
| Unique | 81 ? |
|---|---|
| Unique (%) | 100.0% |
Histogram of lengths of the category
Length
| Max length | 16 |
|---|---|
| Median length | 7 |
| Mean length | 7.75308642 |
| Min length | 4 |
Country
Categorical
| Distinct | 36 |
|---|---|
| Distinct (%) | 44.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 648.0 B |
| China | |
|---|---|
| India | |
| United States | |
| Japan | |
| Brazil | 3 |
| Other values (31) |
| Value | Count | Frequency (%) | |
| China | 20 | 24.7% | |
| India | 9 | 11.1% | |
| United States | 9 | 11.1% | |
| Japan | 4 | 4.9% | |
| Brazil | 3 | 3.7% | |
| Pakistan | 2 | 2.5% | |
| Russia | 2 | 2.5% | |
| Spain | 2 | 2.5% | |
| Mexico | 2 | 2.5% | |
| Egypt | 2 | 2.5% | |
| Other values (26) | 26 | 32.1% |
Frequencies of value counts
Unique
| Unique | 26 ? |
|---|---|
| Unique (%) | 32.1% |
Histogram of lengths of the category
Length
| Max length | 14 |
|---|---|
| Median length | 5 |
| Mean length | 6.962962963 |
| Min length | 4 |
UN 2018 population estimates[b]
Real number (ℝ≥0)
| Distinct | 79 |
|---|---|
| Distinct (%) | 97.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 10736739.11 |
|---|---|
| Minimum | 5023000 |
| Maximum | 37400068 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 5023000 |
|---|---|
| 5-th percentile | 5207000 |
| Q1 | 6115000 |
| median | 8245000 |
| Q3 | 13215000 |
| 95-th percentile | 21650000 |
| Maximum | 37400068 |
| Range | 32377068 |
| Interquartile range (IQR) | 7100000 |
Descriptive statistics
| Standard deviation | 6276119.813 |
|---|---|
| Coefficient of variation (CV) | 0.5845461781 |
| Kurtosis | 3.741088261 |
| Mean | 10736739.11 |
| Median Absolute Deviation (MAD) | 2673000 |
| Skewness | 1.784842227 |
| Sum | 869675868 |
| Variance | 3.938967991e+13 |
| Monotocity | Decreasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 7236000 | 2 | 2.5% | |
| 6115000 | 2 | 2.5% | |
| 5972000 | 1 | 1.2% | |
| 21650000 | 1 | 1.2% | |
| 5157000 | 1 | 1.2% | |
| 5695000 | 1 | 1.2% | |
| 13171000 | 1 | 1.2% | |
| 11908000 | 1 | 1.2% | |
| 8864000 | 1 | 1.2% | |
| 12410000 | 1 | 1.2% | |
| Other values (69) | 69 | 85.2% |
| Value | Count | Frequency (%) | |
| 5023000 | 1 | 1.2% | |
| 5052000 | 1 | 1.2% | |
| 5086000 | 1 | 1.2% | |
| 5157000 | 1 | 1.2% | |
| 5207000 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 37400068 | 1 | 1.2% | |
| 28514000 | 1 | 1.2% | |
| 25674800 | 1 | 1.2% | |
| 25582000 | 1 | 1.2% | |
| 21650000 | 1 | 1.2% |
| Distinct | 25 |
|---|---|
| Distinct (%) | 33.3% |
| Missing | 6 |
| Missing (%) | 7.4% |
| Memory size | 648.0 B |
| Municipality | |
|---|---|
| City (sub - provincial) | |
| City | |
| Capital city | |
| Designated city | |
| Other values (20) |
| Value | Count | Frequency (%) | |
| Municipality | 20 | 24.7% | |
| City (sub - provincial) | 14 | 17.3% | |
| City | 8 | 9.9% | |
| Capital city | 4 | 4.9% | |
| Designated city | 3 | 3.7% | |
| Urban governorate | 3 | 3.7% | |
| Metropolitan municipality | 3 | 3.7% | |
| Federal city | 2 | 2.5% | |
| Metropolitan city | 2 | 2.5% | |
| Country | 1 | 1.2% | |
| Other values (15) | 15 | 18.5% | |
| (Missing) | 6 | 7.4% |
Frequencies of value counts
Unique
| Unique | 16 ? |
|---|---|
| Unique (%) | 21.3% |
Histogram of lengths of the category
Length
| Max length | 29 |
|---|---|
| Median length | 12 |
| Mean length | 15.02469136 |
| Min length | 3 |
| Distinct | 74 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 7 |
| Missing (%) | 8.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 7783677.095 |
|---|---|
| Minimum | 236453 |
| Maximum | 32054159 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 236453 |
|---|---|
| 5-th percentile | 680455.05 |
| Q1 | 2726647.25 |
| median | 7697000 |
| Q3 | 10515511.75 |
| 95-th percentile | 16292687.25 |
| Maximum | 32054159 |
| Range | 31817706 |
| Interquartile range (IQR) | 7788864.5 |
Descriptive statistics
| Standard deviation | 5873206.433 |
|---|---|
| Coefficient of variation (CV) | 0.7545542244 |
| Kurtosis | 3.493254746 |
| Mean | 7783677.095 |
| Median Absolute Deviation (MAD) | 4097937 |
| Skewness | 1.408052735 |
| Sum | 575992105 |
| Variance | 3.449455381e+13 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 2165867 | 1 | 1.2% | |
| 13515271 | 1 | 1.2% | |
| 2725006 | 1 | 1.2% | |
| 8126755 | 1 | 1.2% | |
| 16753235 | 1 | 1.2% | |
| 3054300 | 1 | 1.2% | |
| 13200000 | 1 | 1.2% | |
| 16044700 | 1 | 1.2% | |
| 12528300 | 1 | 1.2% | |
| 6727000 | 1 | 1.2% | |
| Other values (64) | 64 | 79.0% | |
| (Missing) | 7 | 8.6% |
| Value | Count | Frequency (%) | |
| 236453 | 1 | 1.2% | |
| 420003 | 1 | 1.2% | |
| 470914 | 1 | 1.2% | |
| 639598 | 1 | 1.2% | |
| 702455 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 32054159 | 1 | 1.2% | |
| 24870895 | 1 | 1.2% | |
| 21893095 | 1 | 1.2% | |
| 16753235 | 1 | 1.2% | |
| 16044700 | 1 | 1.2% |
| Distinct | 74 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 7 |
| Missing (%) | 8.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 4935.378378 |
|---|---|
| Minimum | 22 |
| Maximum | 82403 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 22 |
|---|---|
| 5-th percentile | 103.6 |
| Q1 | 358 |
| median | 1307 |
| Q3 | 3768.5 |
| 95-th percentile | 16475.75 |
| Maximum | 82403 |
| Range | 82381 |
| Interquartile range (IQR) | 3410.5 |
Descriptive statistics
| Standard deviation | 11766.34136 |
|---|---|
| Coefficient of variation (CV) | 2.384080906 |
| Kurtosis | 29.10374431 |
| Mean | 4935.378378 |
| Median Absolute Deviation (MAD) | 1006 |
| Skewness | 5.038478978 |
| Sum | 365218 |
| Variance | 138446788.9 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 10135 | 1 | 1.2% | |
| 464 | 1 | 1.2% | |
| 650 | 1 | 1.2% | |
| 1572 | 1 | 1.2% | |
| 751 | 1 | 1.2% | |
| 589 | 1 | 1.2% | |
| 82403 | 1 | 1.2% | |
| 116 | 1 | 1.2% | |
| 243 | 1 | 1.2% | |
| 2672 | 1 | 1.2% | |
| Other values (64) | 64 | 79.0% | |
| (Missing) | 7 | 8.6% |
| Value | Count | Frequency (%) | |
| 22 | 1 | 1.2% | |
| 43 | 1 | 1.2% | |
| 93 | 1 | 1.2% | |
| 101 | 1 | 1.2% | |
| 105 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 82403 | 1 | 1.2% | |
| 53068 | 1 | 1.2% | |
| 22142 | 1 | 1.2% | |
| 16596 | 1 | 1.2% | |
| 16411 | 1 | 1.2% |
| Distinct | 74 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 7 |
| Missing (%) | 8.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 7261.108108 |
|---|---|
| Minimum | 29 |
| Maximum | 41399 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 29 |
|---|---|
| 5-th percentile | 614.85 |
| Q1 | 1890 |
| median | 5163 |
| Q3 | 10756.25 |
| 95-th percentile | 20541.9 |
| Maximum | 41399 |
| Range | 41370 |
| Interquartile range (IQR) | 8866.25 |
Descriptive statistics
| Standard deviation | 7196.955453 |
|---|---|
| Coefficient of variation (CV) | 0.9911648947 |
| Kurtosis | 6.187297639 |
| Mean | 7261.108108 |
| Median Absolute Deviation (MAD) | 3747.5 |
| Skewness | 2.057300883 |
| Sum | 537322 |
| Variance | 51796167.8 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 10759 | 1 | 1.2% | |
| 26349 | 1 | 1.2% | |
| 1525 | 1 | 1.2% | |
| 4336 | 1 | 1.2% | |
| 1186 | 1 | 1.2% | |
| 29 | 1 | 1.2% | |
| 849 | 1 | 1.2% | |
| 20694 | 1 | 1.2% | |
| 18671 | 1 | 1.2% | |
| 6202 | 1 | 1.2% | |
| Other values (64) | 64 | 79.0% | |
| (Missing) | 7 | 8.6% |
| Value | Count | Frequency (%) | |
| 29 | 1 | 1.2% | |
| 200 | 1 | 1.2% | |
| 389 | 1 | 1.2% | |
| 570 | 1 | 1.2% | |
| 639 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 41399 | 1 | 1.2% | |
| 26349 | 1 | 1.2% | |
| 21935 | 1 | 1.2% | |
| 20694 | 1 | 1.2% | |
| 20460 | 1 | 1.2% |
| Distinct | 41 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 40 |
| Missing (%) | 49.4% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 13498899.56 |
|---|---|
| Minimum | 5156217 |
| Maximum | 37274000 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 5156217 |
|---|---|
| 5-th percentile | 5286642 |
| Q1 | 6641649 |
| median | 12545272 |
| Q3 | 19303000 |
| 95-th percentile | 29000000 |
| Maximum | 37274000 |
| Range | 32117783 |
| Interquartile range (IQR) | 12661351 |
Descriptive statistics
| Standard deviation | 8183468.659 |
|---|---|
| Coefficient of variation (CV) | 0.60623228 |
| Kurtosis | 0.8646083074 |
| Mean | 13498899.56 |
| Median Absolute Deviation (MAD) | 6245272 |
| Skewness | 1.149962656 |
| Sum | 553454882 |
| Variance | 6.696915929e+13 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=41)
| Value | Count | Frequency (%) | |
| 12644321 | 1 | 1.2% | |
| 5474482 | 1 | 1.2% | |
| 6997384 | 1 | 1.2% | |
| 21804515 | 1 | 1.2% | |
| 12806866 | 1 | 1.2% | |
| 6300000 | 1 | 1.2% | |
| 21734682 | 1 | 1.2% | |
| 7200000 | 1 | 1.2% | |
| 6096120 | 1 | 1.2% | |
| 5949951 | 1 | 1.2% | |
| Other values (31) | 31 | 38.3% | |
| (Missing) | 40 | 49.4% |
| Value | Count | Frequency (%) | |
| 5156217 | 1 | 1.2% | |
| 5274321 | 1 | 1.2% | |
| 5286642 | 1 | 1.2% | |
| 5474482 | 1 | 1.2% | |
| 5928040 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 37274000 | 1 | 1.2% | |
| 33430285 | 1 | 1.2% | |
| 29000000 | 1 | 1.2% | |
| 25514000 | 1 | 1.2% | |
| 24400000 | 1 | 1.2% |
| Distinct | 31 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 50 |
| Missing (%) | 61.7% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 9579.709677 |
|---|---|
| Minimum | 620 |
| Maximum | 22463 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 620 |
|---|---|
| 5-th percentile | 1511 |
| Q1 | 4067.5 |
| median | 7762 |
| Q3 | 14427.5 |
| 95-th percentile | 21542.5 |
| Maximum | 22463 |
| Range | 21843 |
| Interquartile range (IQR) | 10360 |
Descriptive statistics
| Standard deviation | 6512.7113 |
|---|---|
| Coefficient of variation (CV) | 0.6798443293 |
| Kurtosis | -0.8408447092 |
| Mean | 9579.709677 |
| Median Absolute Deviation (MAD) | 4797 |
| Skewness | 0.5625595673 |
| Sum | 296971 |
| Variance | 42415408.48 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=31)
| Value | Count | Frequency (%) | |
| 5327 | 1 | 1.2% | |
| 2819 | 1 | 1.2% | |
| 3780 | 1 | 1.2% | |
| 620 | 1 | 1.2% | |
| 1171 | 1 | 1.2% | |
| 7256 | 1 | 1.2% | |
| 3560 | 1 | 1.2% | |
| 15890 | 1 | 1.2% | |
| 15403 | 1 | 1.2% | |
| 17009 | 1 | 1.2% | |
| Other values (21) | 21 | 25.9% | |
| (Missing) | 50 | 61.7% |
| Value | Count | Frequency (%) | |
| 620 | 1 | 1.2% | |
| 1171 | 1 | 1.2% | |
| 1851 | 1 | 1.2% | |
| 2793 | 1 | 1.2% | |
| 2819 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 22463 | 1 | 1.2% | |
| 21690 | 1 | 1.2% | |
| 21395 | 1 | 1.2% | |
| 18640 | 1 | 1.2% | |
| 17315 | 1 | 1.2% |
| Distinct | 31 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 50 |
| Missing (%) | 61.7% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 3349.225806 |
|---|---|
| Minimum | 274 |
| Maximum | 20770 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 274 |
|---|---|
| 5-th percentile | 330 |
| Q1 | 774 |
| median | 2094 |
| Q3 | 3083.5 |
| 95-th percentile | 13129.5 |
| Maximum | 20770 |
| Range | 20496 |
| Interquartile range (IQR) | 2309.5 |
Descriptive statistics
| Standard deviation | 4746.479437 |
|---|---|
| Coefficient of variation (CV) | 1.417187049 |
| Kurtosis | 7.833121455 |
| Mean | 3349.225806 |
| Median Absolute Deviation (MAD) | 1301 |
| Skewness | 2.771906778 |
| Sum | 103826 |
| Variance | 22529067.05 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=31)
| Value | Count | Frequency (%) | |
| 7583 | 1 | 1.2% | |
| 5603 | 1 | 1.2% | |
| 2180 | 1 | 1.2% | |
| 2772 | 1 | 1.2% | |
| 2374 | 1 | 1.2% | |
| 1058 | 1 | 1.2% | |
| 2094 | 1 | 1.2% | |
| 1288 | 1 | 1.2% | |
| 510 | 1 | 1.2% | |
| 462 | 1 | 1.2% | |
| Other values (21) | 21 | 25.9% | |
| (Missing) | 50 | 61.7% |
| Value | Count | Frequency (%) | |
| 274 | 1 | 1.2% | |
| 327 | 1 | 1.2% | |
| 333 | 1 | 1.2% | |
| 368 | 1 | 1.2% | |
| 388 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 20770 | 1 | 1.2% | |
| 17933 | 1 | 1.2% | |
| 8326 | 1 | 1.2% | |
| 7583 | 1 | 1.2% | |
| 5603 | 1 | 1.2% |
| Distinct | 80 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 1 |
| Missing (%) | 1.2% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 11852275 |
|---|---|
| Minimum | 2280000 |
| Maximum | 39105000 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 2280000 |
|---|---|
| 5-th percentile | 4727400 |
| Q1 | 6521000 |
| median | 9143500 |
| Q3 | 15479500 |
| 95-th percentile | 22568800 |
| Maximum | 39105000 |
| Range | 36825000 |
| Interquartile range (IQR) | 8958500 |
Descriptive statistics
| Standard deviation | 7303674.44 |
|---|---|
| Coefficient of variation (CV) | 0.6162255297 |
| Kurtosis | 2.529618677 |
| Mean | 11852275 |
| Median Absolute Deviation (MAD) | 3791000 |
| Skewness | 1.479104659 |
| Sum | 948182000 |
| Variance | 5.334366033e+13 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 14678000 | 1 | 1.2% | |
| 11564000 | 1 | 1.2% | |
| 5103000 | 1 | 1.2% | |
| 11027000 | 1 | 1.2% | |
| 22394000 | 1 | 1.2% | |
| 23971000 | 1 | 1.2% | |
| 11920000 | 1 | 1.2% | |
| 9274000 | 1 | 1.2% | |
| 7026000 | 1 | 1.2% | |
| 21489000 | 1 | 1.2% | |
| Other values (70) | 70 | 86.4% |
| Value | Count | Frequency (%) | |
| 2280000 | 1 | 1.2% | |
| 3994000 | 1 | 1.2% | |
| 4381000 | 1 | 1.2% | |
| 4583000 | 1 | 1.2% | |
| 4735000 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 39105000 | 1 | 1.2% | |
| 35362000 | 1 | 1.2% | |
| 31870000 | 1 | 1.2% | |
| 23971000 | 1 | 1.2% | |
| 22495000 | 1 | 1.2% |
| Distinct | 80 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 1 |
| Missing (%) | 1.2% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2324.275 |
|---|---|
| Minimum | 238 |
| Maximum | 12093 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 238 |
|---|---|
| 5-th percentile | 357.65 |
| Q1 | 1025.25 |
| median | 1646 |
| Q3 | 3064.75 |
| 95-th percentile | 6383.75 |
| Maximum | 12093 |
| Range | 11855 |
| Interquartile range (IQR) | 2039.5 |
Descriptive statistics
| Standard deviation | 2095.803456 |
|---|---|
| Coefficient of variation (CV) | 0.9017020172 |
| Kurtosis | 5.714701572 |
| Mean | 2324.275 |
| Median Absolute Deviation (MAD) | 747 |
| Skewness | 2.103572389 |
| Sum | 185942 |
| Variance | 4392392.126 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 5429 | 1 | 1.2% | |
| 5278 | 1 | 1.2% | |
| 1614 | 1 | 1.2% | |
| 1722 | 1 | 1.2% | |
| 1005 | 1 | 1.2% | |
| 360 | 1 | 1.2% | |
| 290 | 1 | 1.2% | |
| 694 | 1 | 1.2% | |
| 1147 | 1 | 1.2% | |
| 238 | 1 | 1.2% | |
| Other values (70) | 70 | 86.4% |
| Value | Count | Frequency (%) | |
| 238 | 1 | 1.2% | |
| 290 | 1 | 1.2% | |
| 293 | 1 | 1.2% | |
| 313 | 1 | 1.2% | |
| 360 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 12093 | 1 | 1.2% | |
| 8231 | 1 | 1.2% | |
| 7400 | 1 | 1.2% | |
| 7006 | 1 | 1.2% | |
| 6351 | 1 | 1.2% |
| Distinct | 80 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 1 |
| Missing (%) | 1.2% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 8086.8875 |
|---|---|
| Minimum | 734 |
| Maximum | 36928 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 648.0 B |
Quantile statistics
| Minimum | 734 |
|---|---|
| 5-th percentile | 1323.75 |
| Q1 | 4036.5 |
| median | 5743 |
| Q3 | 10012.5 |
| 95-th percentile | 21464.7 |
| Maximum | 36928 |
| Range | 36194 |
| Interquartile range (IQR) | 5976 |
Descriptive statistics
| Standard deviation | 6723.32797 |
|---|---|
| Coefficient of variation (CV) | 0.8313863609 |
| Kurtosis | 5.374588538 |
| Mean | 8086.8875 |
| Median Absolute Deviation (MAD) | 2355.5 |
| Skewness | 2.098478161 |
| Sum | 646951 |
| Variance | 45203138.99 |
| Monotocity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) | |
| 8839 | 1 | 1.2% | |
| 16577 | 1 | 1.2% | |
| 8141 | 1 | 1.2% | |
| 3766 | 1 | 1.2% | |
| 1378 | 1 | 1.2% | |
| 11627 | 1 | 1.2% | |
| 32309 | 1 | 1.2% | |
| 9844 | 1 | 1.2% | |
| 5033 | 1 | 1.2% | |
| 10092 | 1 | 1.2% | |
| Other values (70) | 70 | 86.4% |
| Value | Count | Frequency (%) | |
| 734 | 1 | 1.2% | |
| 1049 | 1 | 1.2% | |
| 1286 | 1 | 1.2% | |
| 1319 | 1 | 1.2% | |
| 1324 | 1 | 1.2% |
| Value | Count | Frequency (%) | |
| 36928 | 1 | 1.2% | |
| 32309 | 1 | 1.2% | |
| 25510 | 1 | 1.2% | |
| 22010 | 1 | 1.2% | |
| 21436 | 1 | 1.2% |
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.Cramér's V (φc)
Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.First rows
| City[a] | Country | UN 2018 population estimates[b] | City proper[c] Definition | City proper[c] Population | City proper[c] Area (km2) | City proper[c] Density (/km2) | Metropolitan area[d] Population | Metropolitan area[d] Area (km2) | Metropolitan area[d] Density (/km2) | Urban area[12] Population | Urban area[12] Area (km2) | Urban area[12] Density (/km2) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Tokyo | Japan | 37400068 | Metropolis prefecture | 13515271.0 | 2191.0 | 6169.0 | 37274000.0 | 13452.0 | 2771.0 | 39105000.0 | 8231.0 | 4751.0 |
| 1 | Delhi | India | 28514000 | Capital City | 16753235.0 | 1484.0 | 11289.0 | 29000000.0 | 3483.0 | 8326.0 | 31870000.0 | 2233.0 | 14272.0 |
| 2 | Seoul | South Korea | 25674800 | Special city | 10013781.0 | 605.0 | 16208.0 | 25514000.0 | 11704.0 | 2180.0 | 22394000.0 | 2769.0 | 8087.0 |
| 3 | Shanghai | China | 25582000 | Municipality | 24870895.0 | 6341.0 | 3922.0 | NaN | NaN | NaN | 22118000.0 | 4069.0 | 5436.0 |
| 4 | São Paulo | Brazil | 21650000 | Municipality | 12252023.0 | 1521.0 | 8055.0 | 21734682.0 | 7947.0 | 2735.0 | 22495000.0 | 3237.0 | 6949.0 |
| 5 | Mexico City | Mexico | 21581000 | City - state | 9209944.0 | 1485.0 | 6202.0 | 21804515.0 | 7866.0 | 2772.0 | 21505000.0 | 2385.0 | 9017.0 |
| 6 | Cairo | Egypt | 20076000 | Urban governorate | 9500000.0 | 3085.0 | 3079.0 | NaN | NaN | NaN | 19787000.0 | 2010.0 | 9844.0 |
| 7 | Mumbai | India | 19980000 | Municipality | 12478447.0 | 603.0 | 20694.0 | 24400000.0 | 4355.0 | 5603.0 | 22186000.0 | 1008.0 | 22010.0 |
| 8 | Beijing | China | 19618000 | Municipality | 21893095.0 | 16411.0 | 1334.0 | NaN | NaN | NaN | 19437000.0 | 4172.0 | 4659.0 |
| 9 | Dhaka | Bangladesh | 19578000 | Capital city | 8906039.0 | 338.0 | 26349.0 | 14543124.0 | NaN | NaN | 16839000.0 | 456.0 | 36928.0 |
Last rows
| City[a] | Country | UN 2018 population estimates[b] | City proper[c] Definition | City proper[c] Population | City proper[c] Area (km2) | City proper[c] Density (/km2) | Metropolitan area[d] Population | Metropolitan area[d] Area (km2) | Metropolitan area[d] Density (/km2) | Urban area[12] Population | Urban area[12] Area (km2) | Urban area[12] Density (/km2) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 71 | Barcelona | Spain | 5494000 | Municipality | 1620343.0 | 101.0 | 15980.0 | 5474482.0 | NaN | NaN | 4735000.0 | 1072.0 | 4417.0 |
| 72 | Johannesburg | South Africa | 5486000 | Metropolitan municipality | NaN | NaN | NaN | NaN | NaN | NaN | 14167000.0 | 4040.0 | 3507.0 |
| 73 | Saint Petersburg | Russia | 5383000 | Federal city | NaN | NaN | NaN | NaN | NaN | NaN | 5207000.0 | 1373.0 | 3792.0 |
| 74 | Qingdao | China | 5381000 | City (sub - provincial) | NaN | NaN | NaN | NaN | NaN | NaN | 6232000.0 | 1655.0 | 3766.0 |
| 75 | Dalian | China | 5300000 | City (sub - provincial) | NaN | NaN | NaN | NaN | NaN | NaN | 3994000.0 | 987.0 | 4047.0 |
| 76 | Washington, D.C. | United States | 5207000 | Federal district | 702455.0 | 177.0 | 3969.0 | 6263245.0 | 17009.0 | 368.0 | 7583000.0 | 5501.0 | 1378.0 |
| 77 | Yangon | Myanmar | 5157000 | City | NaN | NaN | NaN | NaN | NaN | NaN | 6497000.0 | 603.0 | 10774.0 |
| 78 | Alexandria | Egypt | 5086000 | Urban governorate | NaN | NaN | NaN | NaN | NaN | NaN | 4857000.0 | 293.0 | 16577.0 |
| 79 | Jinan | China | 5052000 | City (sub - provincial) | 8700000.0 | 10244.0 | 849.0 | NaN | NaN | NaN | 4381000.0 | 798.0 | 5490.0 |
| 80 | Guadalajara | Mexico | 5023000 | Municipality | 1385621.0 | 151.0 | 9176.0 | 5286642.0 | 3560.0 | 1485.0 | 5437000.0 | 313.0 | 17371.0 |